方法对比
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| 联邦主动学习× | 联邦学习× | |
|---|---|---|
| 领域≠ | 机器学习 | 隐私 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020s | 2017 |
| 提出者≠ | Multiple authors (federated active learning emerged ~2020) | McMahan et al. |
| 类型≠ | Hybrid paradigm (active querying within distributed training) | Distributed privacy-preserving machine learning |
| 开创性文献≠ | Ro, J. Y., Ali, A., Lin, Z., & Suresh, A. T. (2021). Scaling Federated Learning for Fine-tuning of Large Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). link ↗ | McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282. link ↗ |
| 别名 | Federated Active Learning, FAL, Active Federated Learning, distributed active learning | Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme |
| 相关≠ | 6 | 3 |
| 摘要≠ | Federated Active Learning combines the annotation-efficiency of active learning with the privacy-preserving decentralization of federated learning. A shared global model is trained across distributed clients, each of which independently ranks its unlabeled local data and requests labels only for the most informative examples, keeping raw data on-device throughout. | Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model. |
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